Social tagging systems have grown in popularity over the Web in the last years on account of their simplicity to categorize and retrieve content using open-ended tags. The increasing number of users providing information about themselves through social tagging activities caused the emergence of tag-based profiling approaches, which assume that users expose their preferences for certain contents through tag assignments. Thus, the tagging information can be used to make recommendations. This paper presents an overview of the field of social tagging systems which can be used for extending the capabilities of recommender systems. Various limitations of the current generation of social tagging systems and possible extensions that can provide better recommendation capabilities are also considered.

@article{milicevic2010social,
author = {Milicevic, Aleksandra and Nanopoulos, Alexandros and Ivanovic, Mirjana},
title = {Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions},
journal = {Artificial Intelligence Review},
publisher = {Springer Netherlands},
year = {2010},
volume = {33},
number = {3},
pages = {187--209},
url = {http://dx.doi.org/10.1007/s10462-009-9153-2},
doi = {10.1007/s10462-009-9153-2},
issn = {0269-2821},
keywords = {tagging, recommender, collaborative, survey, social},
abstract = {Social tagging systems have grown in popularity over the Web in the last years on account of their simplicity to categorize and retrieve content using open-ended tags. The increasing number of users providing information about themselves through social tagging activities caused the emergence of tag-based profiling approaches, which assume that users expose their preferences for certain contents through tag assignments. Thus, the tagging information can be used to make recommendations. This paper presents an overview of the field of social tagging systems which can be used for extending the capabilities of recommender systems. Various limitations of the current generation of social tagging systems and possible extensions that can provide better recommendation capabilities are also considered.}
}